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Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering

Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as...

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Autores principales: Kashihara, Jun, Takebayashi, Yoshitake, Kunisato, Yoshihiko, Ito, Masaya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409670/
https://www.ncbi.nlm.nih.gov/pubmed/34469469
http://dx.doi.org/10.1371/journal.pone.0256902
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author Kashihara, Jun
Takebayashi, Yoshitake
Kunisato, Yoshihiko
Ito, Masaya
author_facet Kashihara, Jun
Takebayashi, Yoshitake
Kunisato, Yoshihiko
Ito, Masaya
author_sort Kashihara, Jun
collection PubMed
description Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (M(age) = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.
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spelling pubmed-84096702021-09-02 Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering Kashihara, Jun Takebayashi, Yoshitake Kunisato, Yoshihiko Ito, Masaya PLoS One Research Article Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (M(age) = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes. Public Library of Science 2021-09-01 /pmc/articles/PMC8409670/ /pubmed/34469469 http://dx.doi.org/10.1371/journal.pone.0256902 Text en © 2021 Kashihara et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kashihara, Jun
Takebayashi, Yoshitake
Kunisato, Yoshihiko
Ito, Masaya
Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering
title Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering
title_full Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering
title_fullStr Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering
title_full_unstemmed Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering
title_short Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering
title_sort classifying patients with depressive and anxiety disorders according to symptom network structures: a gaussian graphical mixture model-based clustering
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409670/
https://www.ncbi.nlm.nih.gov/pubmed/34469469
http://dx.doi.org/10.1371/journal.pone.0256902
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